Video segmentation using statistical pixel modeling

ABSTRACT

A method for segmenting video data into foreground and background portions utilizes statistical modeling of the pixels. A statistical model of the background is built for each pixel, and each pixel in an incoming video frame is compared with the background statistical model for that pixel. Pixels are determined to be foreground or background based on the comparisons. The method for segmenting video data may be further incorporated into a method for implementing an intelligent video surveillance system.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is a continuation-in-part of co-pending U.S.application Ser. No. 09/815,385, filed on Mar. 23, 2001,commonly-assigned, and incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

[0002] The present invention relates to processing of video frames foruse in video processing systems, for example, intelligent videosurveillance (IVS) systems that are used as a part of or in conjunctionwith Closed Circuit Television Systems (CCTV) that are utilized insecurity, surveillance and related homeland security and anti-terrorismsystems, IVS systems that process surveillance video in retailestablishments for the purposes of establishing in-store human behaviortrends for market research purposes, IVS systems that monitor vehiculartraffic to detect wrong-way traffic, broken-down vehicles, accidents androad blockages, and video compression systems. IVS systems are systemsthat further process video after video segmentation steps to performobject classification in which foreground objects may be classified as ageneral class such as animal, vehicle, or other moving but-unclassifiedobject, or may be classified in more specific classes as human, small-or large-non-human animal, automobile, aircraft, boat, truck, tree,flag, or water region. In IVS systems, once such video segmentation andclassification occurs, then detected objects are processed to determinehow their positions, movements and behaviors relate to user definedvirtual video tripwires, and virtual regions of interest (where a regionof interest may be an entire field of view, or scene). User definedevents that occur will then be flagged as events of interest that willbe communicated to the security officer or professional on duty.Examples of such events include a human or a vehicle crossing a virtualvideo tripwire, a person or vehicle loitering or entering a virtualregion of interest or scene, or an object being left behind or takenaway from a virtual region or scene. In particular, the presentinvention deals with ways of segmenting video frames into theircomponent parts using statistical properties of regions comprising thevideo frames.

BACKGROUND OF THE INVENTION

[0003] In object-based video compression, video segmentation fordetecting and tracking video objects, as well as in other types ofobject-oriented video processing, the input video is separated into twostreams. One stream contains the information representing stationarybackground information, and the other stream contains informationrepresenting the moving portions of the video, to be denoted asforeground information. The background information is represented as abackground model, including a scene model, i.e., a composite imagecomposed from a series of related images, as, for example, one wouldfind in a sequence of video frames; the background model may alsocontain additional models and modeling information. Scene models aregenerated by aligning images (for example, by matching points and/orregions) and determining overlap among them; generation of scene modelsis discussed in further depth in commonly-assigned U.S. patentapplication Ser. No. 09/472,162, filed Dec. 27, 1999, and Ser. No.09/609,919, filed Jul. 3, 2000, both incorporated by reference in theirentireties herein. In an efficient transmission or storage scheme, thescene model need be transmitted only once, while the foregroundinformation is transmitted for each frame. For example, in the case ofan observer (i.e., camera or the like, which is the source of the video)that undergoes only pan, tilt, roll, and zoom types of motion, the scenemodel need be transmitted only once because the appearance of the scenemodel does not change from frame to frame, except in a well-defined waybased on the observer motion, which can be easily accounted for bytransmitting motion parameters. Note that such techniques are alsoapplicable in the case of other forms of motion, besides pan, tilt,roll, and zoom. In IVS systems, the creation of distinct movingforeground and background objects allows the system to attemptclassification on the moving objects of interest, even when thebackground pixels may be undergoing apparent motion due to pan, tilt andzoom motion of the camera.

[0004] To make automatic object-oriented video processing feasible, itis necessary to be able to distinguish the regions in the video sequencethat are moving or changing and to separate (i.e., segment) them fromthe stationary background regions. This segmentation must be performedin the presence of apparent motion, for example, as would be induced bya panning, tilting, rolling, and/or zooming observer (or due to othermotion-related phenomena, including actual observer motion). To accountfor this motion, images are first aligned; that is, correspondinglocations in the images (i.e., frames) are determined, as discussedabove. After this alignment, objects that are truly moving or changing,relative to the stationary background, can be segmented from thestationary objects in the scene. The stationary regions are then used tocreate (or to update) the scene model, and the moving foreground objectsare identified for each frame.

[0005] It is not an easy thing to identify and automatically distinguishbetween video objects that are moving foreground and stationarybackground, particularly in the presence of observer motion, asdiscussed above. Furthermore, to provide the maximum degree ofcompression or the maximum fineness or accuracy of other videoprocessing techniques, it is desirable to segment foreground objects asfinely as possible; this enables, for example, the maintenance ofsmoothness between successive video frames and crispness withinindividual frames. Known techniques have proven, however, to bedifficult to utilize and inaccurate for small foreground objects andhave required excessive processing power and memory. It would,therefore, be desirable to have a technique that permits accuratesegmentation between the foreground and background information andaccurate, crisp representations of the foreground objects, without thelimitations of prior techniques.

SUMMARY OF THE INVENTION

[0006] The present invention is directed to a method for segmentation ofvideo into foreground information and background information, based onstatistical properties of the source video. More particularly, themethod is based on creating and updating statistical informationpertaining to a characteristic of regions of the video and the labelingof those regions (i.e., as foreground or background) based on thestatistical information. For example, in one embodiment, the regions arepixels, and the characteristic is chromatic intensity. Many otherpossibilities exist, as will become apparent. In more particularembodiments, the invention is directed to methods of using the inventivevideo segmentation methods to implement intelligent video surveillancesystems.

[0007] In embodiments of the invention, a background model is developedcontaining at least two components. A first component is the scenemodel, which may be built and updated, for example, as discussed in theaforementioned U.S. patent applications. A second component is abackground statistical model.

[0008] In a first embodiment, the inventive method comprises a two-passprocess of video segmentation. The two passes of the embodiment comprisea first pass in which a background statistical model is built andupdated and a second pass in which regions in the frames are segmented.An embodiment of the first pass comprises steps of aligning each videoframe with a scene model and updating the background statistical modelbased on the aligned frame data. An embodiment of the second passcomprises, for each frame, steps of labeling regions of the frame andperforming spatial filtering.

[0009] In a second embodiment, the inventive method comprises a one-passprocess of video segmentation. The single pass comprises, for each framein a frame sequence of a video stream, steps of aligning the frame witha scene model; building a background statistical model; labeling theregions of the frame, and performing spatial/temporal filtering.

[0010] In yet another embodiment, the inventive method comprises amodified version of the aforementioned one-pass process of videosegmentation. This embodiment is similar to the previous embodiment,except that the step of building a background statistical model isreplaced with a step of building a background statistical model and asecondary statistical model.

[0011] Each of these embodiments may be embodied in the forms of acomputer system running software executing their steps and acomputer-readable medium containing software representing their steps.

Definitions

[0012] In describing the invention, the following definitions areapplicable throughout (including above).

[0013] A “computer” refers to any apparatus that is capable of acceptinga structured input, processing the structured input according toprescribed rules, and producing results of the processing as output.Examples of a computer include: a computer; a general purpose computer;a supercomputer; a mainframe; a super mini-computer; a mini-computer; aworkstation; a micro-computer; a server; an interactive television; ahybrid combination of a computer and an interactive television; andapplication-specific hardware to emulate a computer and/or software. Acomputer can have a single processor or multiple processors, which canoperate in parallel and/or not in parallel. A computer also refers totwo or more computers connected together via a network for transmittingor receiving information between the computers. An example of such acomputer includes a distributed computer system for processinginformation via computers linked by a network.

[0014] A “computer-readable medium” refers to any storage device usedfor storing data accessible by a computer. Examples of acomputer-readable medium include: a magnetic hard disk; a floppy disk;an optical disk, like a CD-ROM or a DVD; a magnetic tape; a memory chip;and a carrier wave used to carry computer-readable electronic data, suchas those used in transmitting and receiving e-mail or in accessing anetwork.

[0015] “Software” refers to prescribed rules to operate a computer.Examples of software include: software; code segments; instructions;computer programs; and programmed logic.

[0016] A “computer system” refers to a system having a computer, wherethe computer comprises a computer-readable medium embodying software tooperate the computer.

[0017] A “network” refers to a number of computers and associateddevices that are connected by communication facilities. A networkinvolves permanent connections such as cables or temporary connectionssuch as those made through telephone or other communication links.Examples of a network include: an internet, such as the Internet; anintranet; a local area network (LAN); a wide area network (WAN); and acombination of networks, such as an internet and an intranet.

[0018] “Video” refers to motion pictures represented in analog and/ordigital form. Examples of video include video feeds from CCTV systems insecurity, surveillance and anti-terrorism applications, television,movies, image sequences from a camera or other observer, andcomputer-generated image sequences. These can be obtained from, forexample, a wired or wireless live feed, a storage device, a firewireinterface, a video digitizer, a video streaming server, device orsoftware component, a computer graphics engine, or a network connection.

[0019] “Video processing” refers to any manipulation of video,including, for example, compression and editing.

[0020] A “frame” refers to a particular image or other discrete unitwithin a video.

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The invention will now be described in further detail inconnection with the attached drawings, in which:

[0022]FIG. 1 shows a flowchart corresponding to an implementation of afirst embodiment of the invention;

[0023]FIGS. 2a and 2 b show flowcharts corresponding to two alternativeembodiments of the labeling step in the flowchart of FIG. 1;

[0024]FIGS. 3a and 3 b show flowcharts corresponding to implementationsof the spatial/temporal filtering step in the flowchart of FIG. 1;

[0025]FIG. 4 shows a flowchart corresponding to an implementation of asecond embodiment of the invention;

[0026]FIG. 5 shows a flowchart corresponding to an implementation of oneof the steps in the flowchart of FIG. 4;

[0027]FIGS. 6a and 6 b together show a flowchart corresponding to animplementation of another one of the steps in the flowchart of FIG. 4;

[0028]FIG. 7 shows a flowchart corresponding to an implementation of athird embodiment of the invention;

[0029]FIGS. 8a and 8 b together show a flowchart corresponding to animplementation of one of the steps in the flowchart of FIG. 7;

[0030]FIG. 9 depicts an embodiment of the invention in the form ofsoftware embodied on a computer-readable medium, which may be part of acomputer system; and

[0031]FIG. 10 depicts a flowchart of a method of implementing anintelligent video surveillance system according to an embodiment of theinvention.

[0032] Note that identical objects are labeled with the same referencenumerals in all of the drawings that contain them.

DETAILED DESCRIPTION OF THE INVENTION

[0033] As discussed above, the present invention is directed to thesegmentation of video streams into foreground information, whichcorresponds to moving objects, and background information, whichcorresponds to the stationary portions of the video. The presentinvention may be embodied in a number of ways, of which three specificones are discussed below. These embodiments are meant to be exemplary,rather than exclusive.

[0034] The ensuing discussion refers to “pixels” and “chromaticintensity;” however, the inventive method is not so limited. Rather, theprocessing may involve any type of region (including regions comprisingmultiple pixels), not just a pixel, and may use any type ofcharacteristic measured with respect to or related to such a region, notjust chromatic intensity.

[0035] 1. First Embodiment—Two-Pass Segmentation

[0036] The first embodiment of the invention is depicted in FIG. 1 andcorresponds to a two-pass method of segmentation. As shown in FIG. 1,the method begins by obtaining a frame (or video) sequence from a videostream (Step 1). The frame sequence preferably includes two or moreframes of the video stream. The frame sequence can be, for example, aportion of the video stream or the entire video stream. As a portion ofthe video stream, the frame sequence can be, for example, one continuoussequence of frames of the video stream or two or more discontinuoussequences of frames of the video stream. As part of the alignment step,the scene model is also built and updated.

[0037] After Step 1, in Step 2, it is determined whether or not allframes have yet been processed. If not, the next frame is taken andaligned with the underlying scene model of the video stream (Step 3);such alignment is discussed above, and more detailed discussions ofalignment techniques may be found, for example, in commonly-assignedU.S. patent application Ser. No. 09/472,162, filed Dec. 27, 1999, andSer. No. 09/609,919, filed Jul. 3, 2000, both incorporated by referencein their entireties herein, as discussed above, as well as in numerousother references.

[0038] The inventive method is based on the use of statistical modelingto determine whether a particular pixel should be classified as being aforeground object or a part thereof or as being the background or a partthereof. Step 4 deals with the building and updating of a statisticalmodel of the background, using each frame aligned in Step 3.

[0039] The statistical model of the present invention comprises first-and second-order statistics. In the ensuing discussion, mean andstandard deviation will be used as such first- and second-orderstatistics; however, this is meant to be merely exemplary of thestatistics that may be used.

[0040] In general, the mean of N samples, {overscore (x)}, is computedby taking the sum of the samples and dividing it by N, i.e.,$\begin{matrix}{{\overset{\_}{x} = \frac{\sum\limits_{i = 1}^{N}X_{i}}{N}},} & (1)\end{matrix}$

[0041] where x_(i) is a particular sample corresponding to a given pixel(or region), which in the present case could be, for example, themeasured chromatic intensity of the i^(th) sample corresponding to thegiven pixel (or region). In the present setting, then, such a mean wouldbe computed for each pixel or region.

[0042] While Eqn. (1) gives the general formula for a sample mean, itmay not always be optimal to use this formula. In video processingapplications, a pixel's sample value may change drastically when anobject moves through the pixel and change (drastically) back to a valuearound its previous value after the moving object is no longer withinthat pixel. In order to address this type of consideration, theinvention utilizes a weighted average, in which the prior values areweighted more heavily than the present value. In particular, thefollowing equation may be used:

{overscore (x)} _(N) =W _(p) {overscore (x)} _(N−1) +W _(n) x _(N),  (2)

[0043] where W_(p) is the weight of the past values and W_(n) is theweight assigned to the newest value. Additionally, {overscore (x)}_(J)represents the weighted average taken over J samples, and x_(K)represents the K^(th) sample. W_(p) and W_(n) may be set to any pair ofvalues between zero and one such that their sum is one and such thatW_(n)<W_(p), so as to guarantee that the past values are more heavilyweighted than the newest value. As an example, the inventors havesuccessfully used W_(p)=0.9 and W_(n)=0.1.

[0044] Standard deviation, a, is determined as the square root of thevariance, σ², of the values under consideration. In general, variance isdetermined by the following formula:

σ² ={overscore (x²)}−( {overscore (x)})²,  (3)

[0045] where {overscore (x²)} represents the average of x²; thus, thestandard deviation is given by

σ={square root}{square root over (x²)}−( {overscore (x)})².  (4)

[0046] Because the inventive method uses running statistics, thisbecomes

σ_(N) ={square root}{square root over ({x²)}} _(N)−({overscore (x_(N))})²,   (4a)

[0047] where {overscore (x_(N))} is as defined in Eqn. (2) above, and{{overscore (x²)}}_(N) is defined as the weighted average of the squaredvalues of the samples, through the N^(th) sample, and is given by

{{overscore (x²)}} _(N) =W _(P) {{overscore (x²)}} _(n−1) +W _(n) x _(N)².  (5)

[0048] As in the case of the weighted average of the sample values, theweights are used to assure that past values are more heavily weightedthan the present value.

[0049] Given this, Step 4 works to create and update the statisticalmodel by computing the value of Eqn. (4a) for each pixel, for eachframe. In Step 4, the values for the pixels are also stored on apixel-by-pixel basis (as opposed to how they are received, i.e., on aframe-by-frame basis); that is, an array of values is compiled for eachpixel over the sequence of frames. Note that in an alternativeembodiment, Step 4 only performs this storage of values.

[0050] Following Step 4, the method returns to Step 2 to check whetheror not all of the frames have been processed. If they have, then themethod proceeds to Step 5, which commences the second pass of theembodiment.

[0051] In Step 5, the statistical background model is finalized. This isdone by using the stored values for each pixel and determining theirmode, the mode being the value that occurs most often. This may beaccomplished, for example, by taking a histogram of the stored valuesand selecting the value for which the histogram has the highest value.The mode of each pixel is then assigned as the value of the backgroundstatistical model for that pixel.

[0052] Following Step 5, the method proceeds to Step 6, which determineswhether or not all of the frames have been processed yet. If not, thenthe method proceeds to Step 7, in which each pixel in the frame islabeled as being a foreground (FG) pixel or a background (BG) pixel. Twoalternative embodiments of the workings of this step are shown in theflowcharts of FIGS. 2a and 2 b.

[0053]FIG. 2a depicts a two decision level method. In FIG. 2a, the pixellabeling Step 7 begins with Step 71, where it is determined whether ornot all of the pixels in the frame have been processed. If not, then themethod proceeds to Step 72 to examine the next pixel. Step 72 determineswhether or not the pixel matches the background statistical model, i.e.,whether the value of the pixel matches the mode for that pixel. This isperformed by taking the absolute difference between the pixel value andthe value of the background statistical model for the pixel (i.e., themode) and comparing it with a threshold; that is,

Δ=|x _(pixel) −m _(pixel)|  (6)

[0054] is compared with a threshold θ. In Eqn. (6), x_(pixel) denotesthe value of the pixel, while m_(pixel) represents the value of thestatistical background model for that pixel.

[0055] The threshold θ may be determined in many ways. For example, itmay be taken to be a function of standard deviation (of the givenpixel), σ. In a particular exemplary embodiment, θ=3σ; in anotherembodiment, θ=Kσ, where K is chosen by the user. As another example, 0may be assigned a predetermined value (again, for each pixel) or onechosen by the user.

[0056] If Δ≦θ, then the pixel value is considered to match thebackground statistical model. In this case, the pixel is labeled asbackground (BG) in Step 73, and the algorithm proceeds back to Step 71.Otherwise, if Δ>θ, then the pixel value is considered not to match thebackground statistical model, and the pixel is labeled as foreground(FG) in Step 74. Again, the algorithm then proceeds back to Step 71. IfStep 71 determines that all of the pixels (in the frame) have beenprocessed, then Step 7 is finished.

[0057]FIG. 2b depicts a three decision level method, labeled 7′. In FIG.2b, the process once again begins with Step 71, a step of determiningwhether or not all pixels have yet been processed. If not, the processconsiders the next pixel to be processed and executes Step 72, the stepof determining whether or not the pixel being processed matches thebackground statistical model; this is done in the same way as in FIG.2a. If yes, then the pixel is labeled as BG (Step 73), and the processloops back to Step 71. If not, then the process proceeds to Step 75;this is where the process of FIG. 2b is distinguished from that of FIG.2a.

[0058] In Step 75, the process determines whether or not the pixel underconsideration is far from matching the background statistical model.This is accomplished via a threshold test similar to Step 72, only inStep 75, θ is given a larger value. As in Step 72, θ may beuser-assigned or predetermined. In one embodiment, θ=Nσ, where N is aeither a predetermined or user-set number, N>K. In another embodiment,N=6.

[0059] If the result of Step 75 is that Δ≦θ, then the pixel is labeledas FG (Step 74). If not, then the pixel is labeled definite foreground(DFG), in Step 76. In each case, the process loops back to Step 71. OnceStep 71 determines that all pixels in the frame have been processed,Step 7′ is complete.

[0060] Returning to FIG. 1, once all of the pixels of a frame have beenlabeled, the process proceeds to Step 8, in which spatial/temporalfiltering is performed. While shown as a sequential step in FIG. 1, Step8 may alternatively be performed in parallel with Step 7. Details ofStep 8 are shown in the flowcharts of FIGS. 3a and 3 b.

[0061] In FIG. 3a, Step 8 commences with a test as to whether or not allthe pixels of the frame have been processed (Step 81). If not, in Step85, the algorithm selects the next pixel, P_(i), for processing andproceeds to Step 82, where it is determined whether or not the pixel islabeled as BG. If it is, then the process goes back to Step 81. If not,then the pixel undergoes further processing in Steps 83 and 84.

[0062] Step 83, neighborhood filtering, is used to correct formisalignments when the images are aligned. If the current image isslightly misaligned with the growing background statistical model, then,particularly near strong edges, the inventive segmentation procedure,using the background statistical model, will label pixels as foreground.Neighborhood filtering will correct for this. An embodiment of Step 83is depicted in the flowchart of FIG. 3b.

[0063] In FIG. 3b, Step 83 begins with Step 831, where a determinationis made of the scene model location, P_(m), corresponding to P_(i).Next, a neighborhood, comprising the pixels, P′_(m), surrounding P_(m)in the scene model, is selected (Step 832). Step 833 next determines ifall of the pixels in the neighborhood have been processed. If yes, Step83 is complete, and the label of P_(i) remains as it was; if not, theprocess proceeds to Step 834, where the next neighborhood pixel P′_(m)is considered. Step 835 then tests to determine whether or not P_(i)matches P′_(m). This matching test is accomplished by executing thelabeling step (Step 7 or 7′) in a modified fashion, using P_(i) as thepixel under consideration and P′_(m) as the “corresponding” backgroundstatistical model point. If the labeling step returns a label of FG orDFG, there is no match, whereas if it returns a label of BG, there is amatch. If there is no match, the process loops back to Step 833; ifthere is a match, then this is an indication that P_(i) might bemislabeled, and the process continues to Step 836. In Step 836, aneighborhood, comprising the pixels, P′_(i), surrounding P_(i) in theframe, is selected, and an analogous process is performed. That is, inStep 833, it is determined whether or not all of the pixels, P′_(i) inthe neighborhood have yet been considered. If yes, then Step 83 iscomplete, and the label of P_(i) remains as it was; if not, then theprocess proceeds to Step 838, where the next neighborhood pixel, P′_(i),is considered. Step 839 tests to determine if P_(m) matches P′_(i); thisis performed analogously to Step 833, with the P′_(i) underconsideration being used as the pixel being considered and P_(m) as its“corresponding” background statistical model point. If it does not, thenthe process loops back to Step 837; if it does, then P_(i) is relabeledas BG, and Step 83 is complete.

[0064] Returning to FIG. 3a, following Step 83, Step 84 is executed, inwhich morphological erosions and dilations are performed. First, apredetermined number, n, of erosions are performed to remove incorrectlylabeled foreground. Note that pixels labeled DFG may not be erodedbecause they represent either a pixel that is almost certainlyforeground. This is followed by n dilations, which restore the pixelsthat were correctly labeled as foreground but were eroded. Finally, asecond predetermined number, m, of dilations are performed to fill inholes in foreground objects. The erosions and dilations may be performedusing conventional erosion and dilation techniques, applied inaccordance with user-specified parameters, and modified, as discussedabove, such that pixels labeled DFG are not eroded.

[0065] In alternative embodiments, Step 84 may comprise filteringtechniques other than or in addition to morphological erosions anddilations. In general, Step 84 may employ any form or forms of spatialand/or temporal filtering.

[0066] Returning to FIG. 1, following Step 8, the algorithm returns toStep 6, to determine whether or not all frames have been processed. Ifyes, then the processing of the frame sequence is complete, and theprocess ends (Step 9).

[0067] This two-pass embodiment has the advantage of relativesimplicity, and it is an acceptable approach for applications notrequiring immediate or low-latency processing. Examples of suchapplications include off-line video compression and non-linear videoediting and forensic processing of security and surveillance video. Onthe other hand, many other applications such as video security andsurveillance in which timely event reporting is critical do have suchrequirements, and the embodiments to be discussed below are tailored toaddress these requirements.

[0068] 2. Second Embodiment—One-Pass Segmentation

[0069]FIG. 4 depicts a flowchart of a one-pass segmentation process,according to a second embodiment of the invention. Comparing FIG. 4 withFIG. 1 (the first embodiment), the second embodiment differs in thatthere is only a single pass of processing for each frame sequence. Thissingle pass, as shown in Steps 2, 3, 31, 32, 8 in FIG. 4, incorporatesthe processes of the second pass (Steps 5-8 in FIG. 1) with the firstpass (Steps 2-4 in FIG. 1), albeit in a modified form, as will bediscussed below.

[0070] As in the case of the first embodiment, the second embodiment(one-pass process), shown in FIG. 4, begins by obtaining a framesequence (Step 1). As in the first embodiment, the process then performsa test to determine whether or not all of the frames have yet beenprocessed (Step 2). Also as in the first embodiment, if the answer isno, then the next frame to be processed is aligned with the scene model(Step 3). As discussed above, the scene model component of thebackground model is built and updated as part of Step 3, so there isalways at least a deterministically-determined value in the backgroundmodel at each location.

[0071] At this point, the process includes a step of building abackground statistical model (Step 31). This differs from Step 4 of FIG.1, and is depicted in further detail in FIG. 5. The process begins witha step of determining whether or not all pixels in the frame beingprocessed have been processed (Step 311). If not, then the processdetermines whether or not the background statistical model is “mature”(Step 312) and “stable” (Step 313).

[0072] The reason for Steps 312 and 313 is that, initially, thestatistical background model will not be sufficiently developed to makeaccurate decisions as to the nature of pixels. To overcome this, somenumber of frames should be processed before pixels are labeled (i.e.,the background statistical model should be “mature”); in one embodimentof the present invention, this is a user-defined parameter. This may beimplemented as a “look-ahead” procedure, in which a limited number offrames are used to accumulate the background statistical model prior topixel labeling (Step 32 in FIG. 4).

[0073] While simply processing a user-defined number of frames maysuffice to provide a mature statistical model, stability is a secondconcern (Step 313), and it depends upon the standard deviation of thebackground statistical model. In particular, as will be discussed below,the statistical background model includes a standard deviation for eachpixel. The statistical model (for a particular pixel) is defined ashaving become “stable” when its variance (or, equivalently, its standarddeviation) is reasonably small. In an embodiment of the presentinvention, Step 313 determines this by comparing the standard deviationwith a user-defined threshold parameter; if the standard deviation isless than this threshold, then the statistical background model (forthat pixel) is determined to be stable.

[0074] As to the flow of Step 31, in FIG. 5, if the backgroundstatistical model is determined to be mature (Step 312), it isdetermined whether or not the background statistical model is stable(Step 313). If either of these tests (Steps 312 and 313) fails, theprocess proceeds to Step 315, in which the background statistical modelof the pixel being processed is updated using the current value of thatpixel. Step 315 will be explained further below.

[0075] If the background statistical model is determined to be bothmature and stable (in Steps 312 and 313), the process proceeds to Step314, where it is determined whether or not the pixel being processedmatches the background statistical model. If yes, then the backgroundstatistical model is updated using the current pixel value (Step 315);if no, then the process loops back to Step 311 to determine if allpixels in the frame have been processed.

[0076] Step 314 operates by determining whether or not the current pixelvalue is within some range of the mean value of the pixel, according tothe current background statistical model. In one embodiment of theinvention, the range is a user-defined range. In yet another embodiment,it is determined to be a user-defined number of standard deviations;i.e., the pixel value, x, matches the background statistical model if

|x _(pixel) −{overscore (x_(pixel))}|≦ Kσ,  (7)

[0077] where K is the user-defined number of standard deviations, σ;x_(pixel) is the current pixel value; and {overscore (x_(pixel))} is themean value of the current pixel in the background statistical model. Thepurpose of performing Step 314 is to ensure, to the extent possible,that only background pixels are used to develop and update thebackground statistical model.

[0078] In Step 315, the background statistical model is updated. In thisembodiment, the background statistical model consists of the mean andstandard deviation of the values for each pixel (over the sequence offrames). These are computed according to Eqns. (2) and (4a) above.

[0079] Following Step 315, the process loops back to Step 311, todetermine if all pixels (in the current frame) have been processed. Onceall of the pixels have been processed, the process proceeds to Step 316,where the background statistical model is finalized. This finalizationconsists of assigning to each pixel its current mean value and standarddeviation (i.e., the result of processing all of the frames up to thatpoint).

[0080] Note that it is possible for the background statistical model fora given pixel never to stabilize. This generally indicates that theparticular pixel is not a background pixel in the sequence of frames,and there is, therefore, no need to assign it a value for the purposesof the background statistical model. Noting that, as discussed above, ascene model is also built and updated, there is always at least adeterministically-determined value associated with each pixel in thebackground model.

[0081] Following Step 316, the process goes to Step 32, as shown in FIG.4, where the pixels in the frame are labeled according to their type(i.e., definite foreground, foreground or background). Step 32 is shownin further detail in the flowchart of FIGS. 6a and 6 b.

[0082] The following concepts are embodied in the description of Step 32to follow. Ideally, labeling would always be done by testing each pixelagainst its corresponding point in the background statistical model, butthis is not always possible. If the background statistical model is notready to use on the basis of number of frames processed (i.e.,“mature”), then the process must fall back on testing against thecorresponding point in the scene model. If the background statisticalmodel is ready to use but has not yet settled down (i.e., is not“stable”), this is a sign that the pixel is varying and should belabeled as being foreground. If the background statistical model has,for some reason (i.e., because it fails to match the scene model orbecause it has become unsettled again), become unusable, the processmust once again fall back on testing against the scene model.

[0083] As shown in FIG. 6a, Step 32 begins with Step 321, where it isdetermined whether or not all pixels (in the current frame) have beenprocessed. If yes, Step 32 is complete; if not, the next pixel isprocessed in Steps 322 et seq.

[0084] Step 322 determines whether or not the background statisticalmodel is mature. This is done in the same manner as in Step 312 of FIG.5, discussed above. If not, the process proceeds to Step 323, where itis determined whether or not the pixel matches the background chromaticdata of the corresponding point of the scene model.

[0085] Step 323 is performed by carrying out a test to determine whetheror not the given pixel falls within some range of the backgroundchromatic data value. This is analogous to Step 314 of FIG. 5,substituting the background chromatic data value for the statisticalmean. The threshold may be determined in a similar fashion(predetermined, user-determined, or the like).

[0086] If Step 323 determines that the pixel does match the backgroundchromatic data, then the pixel is labeled BG (following connector A) inStep 329 of FIG. 6b. From Step 329, the process loops back (viaconnector D) to Step 321.

[0087] If Step 323 determines that the pixel does not match thebackground chromatic data, then the pixel is labeled FG (followingconnector B) in Step 3210 of FIG. 6b. From the Step 3210, the processloops back (via connector D) to Step 321.

[0088] If Step 322 determines that the background statistical model ismature, processing proceeds to Step 324, which determines whether or notthe background statistical model is stable. Step 324 performs this taskin the same manner as Step 313 of FIG. 5, discussed above. If not, theprocess proceeds to Step 325, where it is determined if the backgroundstatistical model was ever stable (i.e., if it was once stable but isnow unstable). If yes, then the process branches to Step 323, and theprocess proceeds from there as described above. If no, the pixel islabeled DFG (following connector C) in Step 3211 of FIG. 6b, after whichthe process loops back (via connector D) to Step 321.

[0089] If Step 324 determines that the background statistical model isstable, the process goes to Step 326. Step 326 tests whether thebackground statistical model matches the background chromatic data.Similar to the previous matching tests above, this test takes anabsolute difference between the value of the background statisticalmodel (i.e., the mean) for the pixel and the background chromatic data(i.e., of the scene model) for the pixel. This absolute difference isthen compared to some threshold value, as above (predetermined,user-determined, or the like).

[0090] If Step 326 determines that there is not a match between thebackground statistical model and the background chromatic data, theprocess branches to Step 323, where processing proceeds in the samefashion as described above. If Step 326, on the other hand, determinesthat there is a match, the process continues to Step 327.

[0091] Step 327 determines whether or not the current pixel matches thebackground statistical model. This step is performed in the same manneras Step 314 of FIG. 5, discussed above. If the current pixel does match(which, as discussed above, is determined by comparing it to the meanvalue corresponding to the current pixel), the pixel is labeled BG(following connector A) in Step 329 of FIG. 6b, and the process thenloops back (via connector D) to Step 321. If not, then further testingis performed in Step 328.

[0092] Step 328 determines whether, given that the current pixel valuedoes not reflect a BG pixel, it reflects a FG pixel or a DFG pixel. Thisis done by determining if the pixel value is far from matching thebackground statistical model. As discussed above, a FG pixel isdistinguished from a BG pixel (in Step 325) by determining if its valuediffers from the mean by more than a particular amount, for example, anumber of standard deviations (see Eqn. (7)). Step 328 applies the sametest, but using a larger range. Again, the threshold may set as apredetermined parameter, as a computed parameter, or as a user-definedparameter, and it may be given in terms of a number of standarddeviations from the mean, i.e.,

|x _(pixel) −{overscore (x_(pixel))}|≦ Nσ,  (8)

[0093] where N is a number greater than K of Eqn. (7). If the pixelvalue lies outside the range defined, for example, by Eqn. (8), it islabeled DFG (following connector C) in Step 3211 of FIG. 6b, and theprocess loops back (via connector D) to Step 321. If it lies within therange, the pixel is labeled FG (following connector B) in Step 3210 ofFIG. 6b, and the process proceeds (via connector D) to Step 321.

[0094] After Step 32 is complete, the process proceeds to Step 8, asshown in FIG. 4, where spatial/temporal filtering is performed on thepixels in the frame. Step 8 is implemented, in this embodiment of theinvention, in the same manner in which it is implemented for thetwo-pass embodiment, except that the pixel labeling algorithm of FIGS.6a and 6 b is used for Steps 833 and 837 of Step 83 (as opposed to thepixel labeling algorithms used in the two-pass embodiment). FollowingStep 8, the process loops back to Step 2, where, if all frames have beenprocessed, the process ends.

[0095] A single-pass approach, like the one present here, has theadvantage of not requiring a second pass, thus, reducing the latencyassociated with the process. This is useful for applications in whichhigh latencies would be detrimental, for example, videoteleconferencing, webcasting, real-time gaming, and the like.

[0096] 3. Third Embodiment—Modified One-Pass Segmentation

[0097] While the one-pass approach described above has a lower latencythan the two-pass approach, it does have a disadvantage in regard to thebackground statistical model. In particular, the cumulative statisticalmodeling approach used in the one-pass embodiment of the invention maystabilize on a non-representative statistical model for an element(i.e., pixel, region, etc.; that is, whatever size element is underconsideration). If the values (e.g., chromatic values) of frame elementscorresponding to a particular element of the video scene fundamentallychange (i.e., something happens to change the video, for example, aparked car driving away, a moving car parking, the lighting changes,etc.), then the scene model element will no longer accurately representthe true scene. This can be addressed by utilizing a mechanism fordynamically updating the background statistical model so that at anygiven time it accurately represents the true nature of the scenedepicted in the video. Such a mechanism is depicted in the embodiment ofthe invention shown in FIG. 7.

[0098] In FIG. 7, Steps 1-3, 32, 8, and 9 are as described in theone-pass embodiment above. The embodiment of FIG. 7 differs from that ofFIG. 4 in that after a given frame is aligned with the scene model (Step3), the process executes Step 310, in which the background statisticalmodel and, simultaneously, a secondary background statistical model arebuilt. Step 310 is more fully described in connection with FIGS. 8a and8 b.

[0099] As shown in FIG. 8a, Step 310 includes all of the steps shown inStep 31 in FIG. 5 (which are shown using the same reference numerals),and it begins with a step of determining whether or not all pixels haveyet been processed (Step 311). If not, the next pixel is processed byproceeding to Step 312. In Step 312, it is determined whether or not thebackground statistical model is mature. If not, the process branches toStep 315, where the pixel is used to update the background statisticalmodel. Following Step 315, the process loops back to Step 311.

[0100] If Step 312 determines that the background statistical model ismature, the process proceeds to Step 313, where it is determined whetheror not the background statistical model is stable. If it is not, then,as in the case of a negative determination in Step 312, the processbranches to Step 315 (and then loops back to Step 311). Otherwise, theprocess proceeds to Step 314.

[0101] In Step 314, it is determined whether or not the pixel underconsideration matches the background statistical model. If it does, theprocess proceeds with Step 315 (and then loops back to Step 311);otherwise, the process executes the steps shown in FIG. 8b, which buildand update a secondary background statistical model. This secondarybackground statistical model is built in parallel with the backgroundstatistical model, as reflected in FIG. 8b; uses the same procedures asare used to build and update the background statistical model; andrepresents the pixel values that do not match the background statisticalmodel.

[0102] Following a negative determination in Step 314, the process thenmakes a determination as to whether or not the secondary backgroundstatistical model is mature (Step 3107). This determination is made inthe same fashion as in Step 313. If not, the process branches to Step3109, where the secondary background statistical model is updated, usingthe same procedures as for the background statistical model (Step 315).From Step 3109, the process loops back to Step 311 (in FIG. 8a).

[0103] If Step 3107 determines that the secondary background statisticalmodel is mature, the process proceeds to Step 3108, which determines(using the same procedures as in Step 314) whether or not the secondarybackground statistical model is stable. If not, the process proceeds toStep 3109 (and from there to Step 311). If yes, then the processbranches to Step 31010, in which the background statistical model isreplaced with the secondary background statistical model, after whichthe process loops back to Step 311. Additionally, concurrently with thereplacement of the background statistical model by the secondarybackground statistical model in Step 31010, the scene model data isreplaced with the mean value of the secondary statistical model. At thispoint, the secondary background statistical model is reset to zero, anda new one will be built using subsequent data.

[0104] This modified one-pass embodiment has the advantage of improvedstatistical accuracy over the one-pass embodiment, and it solves thepotential problem of changing background images. It does this whilestill maintaining improved latency time over the two-pass embodiment,and at only a negligible decrease in processing speed compared with theone-pass embodiment.

[0105] 4. Additional Embodiments and Remarks

[0106] While the above discussion considers two-level and three-levelpixel labeling algorithms, this embodiment is not limited only to thesecases. Indeed, it is contemplated that an arbitrary number of decisionlevels, corresponding to different ranges (i.e., threshold values) maybe used. In such a case, fuzzy or soft-decision logic would be used tomake decisions in subsequent steps of the segmentation process.

[0107] The above discussion primarily discusses pixels and chromaticvalues (which may be RGB, YUV, intensity, etc.); however, as discussedabove, the invention is not limited to these quantities. Regions otherthan pixels may be used, and quantities other than chromatic values maybe used.

[0108] As discussed above, the invention, including all of theembodiments discussed in the preceding sections, may be embodied in theform of a computer system or in the form of a computer-readable mediumcontaining software implementing the invention. This is depicted in FIG.9, which shows a plan view for a computer system for the invention. Thecomputer 91 includes a computer-readable medium 92 embodying softwarefor implementing the invention and/or software to operate the computer91 in accordance with the invention. Computer 91 receives a video streamand outputs segmented video, as shown. Alternatively, the segmentedvideo may be further processed within the computer.

[0109] Also as discussed above, the statistical pixel modeling methodsdescribed above may be incorporated into a method of implementing anintelligent video surveillance system. FIG. 10 depicts an embodiment ofsuch a method. In particular, block 1001 represents the use ofstatistical pixel modeling, e.g., as described above. Once thestatistical pixel modeling has been completed, block 1002 uses theresults to identify and classify objects. Block 1002 may use, forexample, statistical or template-oriented methods for performing suchidentification and classification. In performing identification andclassification, it is determined whether or not a given object is anobject of interest; for example, one may be interested in tracking themovements of people through an area under surveillance, which would makepeople “objects of interest.” In Block 1003, behaviors of objects ofinterest are analyzed; for example, it may be determined if a person hasentered a restricted area. Finally, in Block 1004, if desired, variousnotifications may be sent out or other appropriate actions taken.

[0110] The invention has been described in detail with respect topreferred embodiments, and it will now be apparent from the foregoing tothose skilled in the art that changes and modifications may be madewithout departing from the invention in its broader aspects. Theinvention, therefore, as defined in the appended claims, is intended tocover all such changes and modifications as fall within the true spiritof the invention.

We claim:
 1. A method of implementing an intelligent video surveillancesystem, comprising: obtaining a frame sequence from an input videostream; executing a first-pass method for each frame of the framesequence, the first-pass method comprising the steps of: aligning theframe with a scene model; and updating a background statistical model;and finalizing the background statistical model; executing a second-passmethod for each frame of the frame sequence, the second-pass methodcomprising the steps of: labeling each region of the frame; andperforming spatial/temporal filtering of the regions of the frame;identifying and classifying objects using the labeled and filteredregions; and analyzing behaviors of at least one of the objects.
 2. Acomputer-readable medium comprising software implementing the method ofclaim
 1. 3. An intelligent video surveillance system comprising acomputer system comprising: a computer; and a computer-readable mediumaccording to claim
 2. 4. A method of implementing an intelligent videosurveillance system, comprising: obtaining a frame sequence from a videostream; for each frame in the frame sequence, performing the followingsteps: aligning the frame with a scene model; building a backgroundstatistical model; labeling the regions of the frame; and performingspatial/temporal filtering; identifying and classifying objects based onthe results of the labeling and filtering; and analyzing behaviors of atleast one object.
 5. A computer-readable medium comprising softwareimplementing the method of claim
 4. 6. An intelligent video surveillancesystem comprising a computer system comprising: a computer; and acomputer-readable medium according to claim
 5. 7. A method ofimplementing an intelligent video surveillance system, comprising:obtaining a frame sequence from a video stream; for each frame in theframe sequence, performing the following steps: aligning the frame witha scene model; building a background statistical model and a secondarystatistical model; labeling the regions of the frame; and performingspatial/temporal filtering; identifying and classifying objects based onthe results of the labeling and filtering; and analyzing behaviors of atleast one object.
 8. A computer-readable medium comprising softwareimplementing the method of claim
 7. 9. An intelligent video surveillancesystem comprising a computer system comprising: a computer; and acomputer-readable medium according to claim
 8. 10. A method ofimplementing an intelligent video surveillance system, comprising:segmenting video into foreground and background components, thesegmenting comprising: obtaining a sequence of video frames; buildingand updating at least one background statistical model for each regionof the video frames, based on the video frames; and assigning labels tothe regions, based on the at least one background statistical model;identifying and classifying objects based on the labeled regions; andanalyzing behaviors of at least one object.
 11. A computer-readablemedium comprising software implementing the method of claim
 10. 12. Anintelligent video surveillance system comprising a computer systemcomprising: a computer; and a computer-readable medium according toclaim 11.